Individuals and organizations are rapidly accumulating large collections of digital content, including text, audio, graphics, animated graphics, full-motion video, and other digital content, including business process execution data and monitoring data. This content may be presented individually or combined in a wide variety of different forms, including documents, e-mail messages, alerts, presentations, music, still photographs, commercial videos, home movies, and meta data describing one or more associated digital content files. As these collections grow in number and diversity, individuals and organizations increasingly will require systems and methods for organizing and browsing the digital content in their collections. To meet this need, a variety of different systems and methods for organizing and browsing different kinds of digital content have been proposed. Media object management and business process object management are examples of fields for which digital content organizing and browsing schemes have been developed.
Media Object Management
Many systems allow a user to manually classify images and other digital content by their association with a particular event or subject matter and to segment this digital content based on these classifications. Manual classification systems, however, tend to be time consuming and unwieldy, especially as the size of the digital content collection grows. Some systems are configured to automatically segment digital content, such as images, based on color, shape, or texture features. These automatic segmentation systems, however, often tend to misclassify the digital content because of the inherent inaccuracies associated with classification based on color, shape, or texture features.
A system for automatic albuming of consumer pictures has been proposed that automatically determines the order in which pictures should be presented and provides an initial basis for automating the selection of images to be included in a given album. The system includes modules for image event clustering, dud detection, and duplicate detection. The image event clustering module automatically segments a set of pictures into events and sub-events for layout onto an album page based on date and time information recorded at generation time, as well as image content information. The dud detection module detects very low quality pictures and allows a user to exclude such pictures from the album. The duplicate detection module detects potential duplicate pictures and allows a user to choose a particular one of the detected duplicates for inclusion in the album.
Another digital photograph browsing system applies time-based and content-based clustering algorithms to a collection of digital photographs to identify events in the collection. The time-based clustering algorithm compares a gap in the creation times of the photographs to a local average of temporally nearby gaps. A gap is considered to correspond to a change of event when it is much longer than the local gap average. In order to handle the wide dynamic range of time gaps, the gap detection algorithm operates on logarithmically transformed time gaps. The content-based clustering algorithm is based on probabilistic color histogram models of the digital photographs.
Business Process Object Management
E-business is transforming corporations, markets, and the global economy. The conduct of business over internet (e.g., buying, selling, servicing customers and collaborating with business partners) is affecting how business transactions are performed. Today, web interfaces allow customers to easily find products, services, providers and suppliers that they need, compare prices and qualities, and trade, buy, and get products and services delivered quickly. Customers may be presented with user-friendly graphical interfaces, targeted advertisements, up-to-date product catalogues, and personalized stores. The web facade, however, hides inefficiencies, manual and error-prone operations, and slow, complex, inflexible, and unmanageable systems. Indeed, in many e-business applications, the execution of business processes involves a substantial amount of human intervention in several aspects of business process execution, such as (repeated) data entry, process execution monitoring (a process that often requires tracking each process over several systems in order to find out its current advancement state), exception handling, and scheduling of process activities.
As a result, process design, automation, and management technologies are being used in both traditional and newly-formed, internet-based enterprises in order to improve the quality and efficiency of their administrative and production processes, to manage electronic commerce (or e-commerce) transactions, and to rapidly and reliably deliver services to businesses and individual customers. To this end, various data mining and other data management approaches are being developed to organize and browse the large collections of business process data that is being generated at an ever-increasing rate.